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sidemantic-modeler

Constructs, validates, and manages semantic data models from SQL and YAML.

111|13|Updated Oct 4, 2025
Search Tags:#validation#model management#data lake#semantic modeling#schema automation#SQL reverse engineering
One-click install
npx skills add https://github.com/sidequery/sidemantic --skill sidemantic-modeler
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sidemantic-modeler
Source: https://github.com/sidequery/sidemantic/tree/main/skills/sidemantic-modeler
Command: npx skills add https://github.com/sidequery/sidemantic --skill sidemantic-modeler

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies the building, validation, and management of semantic models, enabling users to create consistent, queryable data layers efficiently.

Core Features & Use Cases

  • Model Construction: Build semantic layers that map physical database schemas to business-friendly definitions.
  • Schema Reverse Engineering: Generate models automatically from SQL queries, reducing manual modeling effort.
  • Validation & Deployment: Validate models for correctness and deploy them into data environments with minimal effort.

Quick Start

Use the sidemantic skill to generate models from a set of SQL files and validate their correctness.

Frequently Asked Questions about sidemantic-modeler

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reverse engineer a semantic model from existing SQL queries?▼

Reverse engineering a semantic model from SQL involves parsing existing query files to automatically generate business-friendly definitions, reducing manual modeling effort. This maps physical schemas to queryable layers for efficient analysis.

What is the best way to validate a semantic data model before deploying it?▼

Validating a semantic data model requires executing checks against best practices using Python libraries for parsing SQL and YAML. This ensures correctness and reliability before deploying into data environments.

How do I map physical database schemas to business-friendly definitions for analytics?▼

Mapping physical schemas to business-friendly definitions is achieved through semantic model construction, which builds a consistent queryable data layer. This streamlines analysis workflows by translating raw database structures into accessible terms.

Do I need Python and YAML to automate schema management for a data lake?▼

Yes, you need Python libraries for parsing SQL and YAML to automate schema management and model validation. These dependencies are required to construct, parse, and execute validation rules according to best practices.

Can I automate deployment for validated semantic models into data environments?▼

Automated deployment for validated semantic models is supported, allowing you to push verified queryable data layers into data environments with minimal effort. This follows validation checks to ensure reliable schema automation.

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